{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T04:07:31Z","timestamp":1790654851924,"version":"4.1.0"},"reference-count":20,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["bjui-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BJU International"],"published-print":{"date-parts":[[2021,3]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To develop and validate the Preoperative Risk Evaluation for Partial Nephrectomy (PREP) score to predict the probability of major postoperative complications after partial nephrectomy (PN) based on patient comorbidities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Patients and Methods<\/jats:title>\n                    <jats:p>The Premier Healthcare Database was used to identify patients who had undergone elective PN. Through review of International Classification of Diseases ninth revision codes, we identified patient comorbidities and major surgical complications (Clavien\u2013Dindo Grade III\u2013V). Multivariable logistic regression was used to identify predictors of major complications. We used half of the set as the training cohort to develop our risk score and the other half as a validation cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>From 2003 to 2015, 25\u00a0451 PNs were performed. The overall rate of major complications was 4.9%. The final risk score consisted of 10 predictors: age, sex, congestive heart failure, coronary artery disease, chronic obstructive pulmonary disease, chronic kidney disease, diabetes, hypertension, obesity, and smoking. In the training cohort, the area under the receiver operating characteristic curve (AUC) was 0.75 (95% confidence interval [CI] 0.73\u20130.78), while the AUC for the validation cohort was 0.73 (95% CI 0.70\u20130.75). The predicted probabilities of major complication in the low\u2010 (\u226410\u00a0points), intermediate\u2010 (11\u201320\u00a0points), high\u2010 (21\u201330\u00a0points), and very high\u2010risk (&gt;30 points) categories were 3% (95% CI 2.6\u20133.2), 8% (95% CI 7.2\u20139.2), 24% (95% CI 20.5\u201327.8), and 41% (95% CI 34.5\u201347.8), respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We developed and validated the PREP score to predict the risk of complications after PN based on patient characteristics. Calculation of the PREP score can help providers select treatment options for patients with a cT1a renal mass and enhance the informed consent process for patients planning to undergo PN.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/bju.15240","type":"journal-article","created":{"date-parts":[[2020,12,4]],"date-time":"2020-12-04T11:49:58Z","timestamp":1607082598000},"page":"369-374","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Patient factors predict complications after partial nephrectomy: validation and calibration of the Preoperative Risk Evaluation for Partial Nephrectomy (PREP) score"],"prefix":"10.1111","volume":"127","author":[{"given":"Melissa J.","family":"Huynh","sequence":"first","affiliation":[{"name":"Division of Urology Brigham and Women\u2019s Hospital Boston MA USA"},{"name":"Harvard Medical School Boston MA 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